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We present a novel approach for the reconstruction of time-resolved free-running first-pass myocardial perfusion MRI, named CMR-MOTUS. This method builds upon the MR-MOTUS framework and addresses the challenges of a contrast varying reference image. By integrating a low-rank plus sparse (L+S) decomposition, CMR-MOTUS efficiently captures both motion fields and contrast variations. This innovative technique eliminates the need for acquiring a motion-static reference image prior to the examination, thereby reducing examination time and complexity for cardiac MRI examinations. Our results demonstrate that CMR-MOTUS can successfully disentangle different dynamic components, offering high-quality motion fields and motion correct a myocardial first-pass perfusion image series.

Purpose: Current 3D Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) protocols use transient-state, gradient-spoiled gradient-echo sequences that are prone to cerebrospinal fluid (CSF) pulsation artifacts when applied to the brain. This study aims at developing a 3D MR-STAT protocol for whole-brain relaxometry that overcomes the challenges posed by CSF-induced ghosting artifacts. Method: We optimized the flip-angle train within the Cartesian 3D MR-STAT framework to achieve two objectives: (1) minimization of the noise level in the reconstructed quantitative maps, and (2) reduction of the CSF-to-white-matter signal ratio to suppress CSF signal and the associated pulsation artifacts. The optimized new sequence was tested on a gel/water-phantom to evaluate the accuracy of the quantitative maps, and on healthy volunteers to explore the effectiveness of the CSF artifact suppression and robustness of the new protocol. Results: A new optimized sequence with both high parameter encoding capability and low CSF intensity was proposed and initially validated in the gel/water-phantom experiment. From in-vivo experiments with five volunteers, the proposed CSF-suppressed sequence shows no CSF ghosting artifacts and overall greatly improved image quality for all quantitative maps compared to the baseline sequence. Statistical analysis indicated low inter-subject and inter-scan variability for quantitative parameters in gray matter and white matter (1.6%-2.4% for T1 and 2.0%-4.6% for T2), demonstrating the robustness of the new sequence. Conclusion: We presented a new 3D MR-STAT sequence with CSF suppression that effectively eliminates CSF pulsation artifacts. The new sequence ensures consistently high-quality, 1mm^3 whole-brain relaxometry within a rapid 5.5-minute scan time.

Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI data is challenging. We have previously developed Spectro-Dynamic MRI which allows the characterization of dynamical systems at a high spatial and temporal resolution directly from k-space data. This work presents an extended Spectro-Dynamic MRI framework that reconstructs 1) time-resolved MR images, 2) time-resolved motion fields, 3) dynamical parameters, and 4) an activation force, at a temporal resolution of 11 ms. An iterative algorithm solves a minimization problem containing four terms: a motion model relating the motion to the fully-sampled k-space data, a dynamical model describing the expected type of dynamics, a data consistency term describing the undersampling pattern, and finally a regularization term for the activation force. We acquired MRI data using a dynamic motion phantom programmed to move like an actively driven linear elastic system, from which all dynamic variables could be accurately reconstructed, regardless of the sampling pattern. The proposed method performed better than a two-step approach, where time-resolved images were first reconstructed from the undersampled data without any information about the motion, followed by a motion estimation step., Comment: 11 pages, 7 figures, 5 supplementary figures, 1 supplementary video. The video can be viewed by downloading the source file under "Other Formats"

The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that allows to track myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments. We present a tracking framework that requires only few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance, including both the acquisition of required data, and the tracking inference. The framework is demonstrated in 2D on a motion phantom, and in vivo on volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated by in silico 3D experiments with a digital motion phantom. The framework was compared with template matching - a reference, image-based, method - and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement. The high accuracy in combination with a total latency of less than 10 ms - including data acquisition and processing - make the proposed method a suitable candidate for tracking during STAR treatments.

Magnetic Resonance Spin Tomography in Time-Domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2 and proton density from one single short scan. A typical 2D MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle train and Cartesian readouts, and the quantitative tissue maps are reconstructed by an iterative, model-based optimization algorithm. In this work, we design a 3D MR-STAT framework based on previous 2D work, in order to achieve better image SNR, higher though-plan resolution and better tissue characterization. Specifically, we design a 7-minute, high-resolution 3D MR-STAT sequence, and the corresponding two-step reconstruction algorithm for the large-scale dataset. To reduce the long acquisition time, Cartesian undersampling strategies such as SENSE are adopted in our transient-state quantitative framework. To reduce the computational burden, a data splitting scheme is designed for decoupling the 3D reconstruction problem into independent 2D reconstructions. The proposed 3D framework is validated by numerical simulations, phantom experiments and in-vivo experiments. High-quality knee quantitative maps with 0.8 x 0.8 x 1.5mm3 resolution and bilateral lower leg maps with 1.6mm isotropic resolution can be acquired using the proposed 7-minute acquisition sequence and the 3-minute-per-slice decoupled reconstruction algorithm. The proposed 3D MR-STAT framework could have wide clinical applications in the future.

In this study, we develop a physics-informed deep learning-based method to synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a single five-minute acquisition and investigate its ability to generalize to arbitrary contrasts to accelerate neuroimaging protocols. A dataset of fifty-five subjects acquired with a standard MRI protocol and a five-minute transient-state sequence was used to develop a physics-informed deep learning-based method. The model, based on a generative adversarial network, maps data acquired from the five-minute scan to "effective" quantitative parameter maps, here named q*-maps, by using its generated PD, T1, and T2 values in a signal model to synthesize four standard contrasts (proton density-weighted, T1-weighted, T2-weighted, and T2-weighted fluid-attenuated inversion recovery), from which losses are computed. The q*-maps are compared to literature values and the synthetic contrasts are compared to an end-to-end deep learning-based method proposed by literature. The generalizability of the proposed method is investigated for five volunteers by synthesizing three non-standard contrasts unseen during training and comparing these to respective ground truth acquisitions via contrast-to-noise ratio and quantitative assessment. The physics-informed method was able to match the high-quality synthMRI of the end-to-end method for the four standard contrasts, with mean \pm standard deviation structural similarity metrics above 0.75 \pm 0.08 and peak signal-to-noise ratios above 22.4 \pm 1.9 and 22.6 \pm 2.1. Additionally, the physics-informed method provided retrospective contrast adjustment, with visually similar signal contrast and comparable contrast-to-noise ratios to the ground truth acquisitions for three sequences unused for model training, demonstrating its generalizability and potential application to accelerate neuroimaging protocols., Comment: 23 pages, 7 figures, 1 table. Presented at ISMRM 2022. Will be submitted to NMR in biomedicine

MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the relationship between the spatial-domain tissue parameters and the time-domain measured signal by using a comprehensive, volumetric forward model. The MR-STAT reconstruction solves a large-scale nonlinear problem, thus is very computationally challenging. In previous work, MR-STAT reconstruction using Cartesian readout data was accelerated by approximating the Hessian matrix with sparse, banded blocks, and can be done on high performance CPU clusters with tens of minutes. In the current work, we propose an accelerated Cartesian MR-STAT algorithm incorporating two different strategies: firstly, a neural network is trained as a fast surrogate to learn the magnetization signal not only in the full time-domain but also in the compressed lowrank domain; secondly, based on the surrogate model, the Cartesian MR-STAT problem is re-formulated and split into smaller sub-problems by the alternating direction method of multipliers. The proposed method substantially reduces the computational requirements for runtime and memory. Simulated and in-vivo balanced MR-STAT experiments show similar reconstruction results using the proposed algorithm compared to the previous sparse Hessian method, and the reconstruction times are at least 40 times shorter. Incorporating sensitivity encoding and regularization terms is straightforward, and allows for better image quality with a negligible increase in reconstruction time. The proposed algorithm could reconstruct both balanced and gradient-spoiled in-vivo data within 3 minutes on a desktop PC, and could thereby facilitate the translation of MR-STAT in clinical settings., Comment: 12 pages, 7 figures, accepted by IEEE Transactions on Medical Imaging (in press)

Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hybrid MR-linac scanner holds the promise to efficiently deal with such respiratory motion through real-time adaptive MR-guided radiotherapy (MRgRT). For MRgRT, motion-fields should be estimated from MR-data and the radiotherapy plan should be adapted in real-time according to the estimated motion-fields. All of this should be performed with a total latency of maximally 200 ms, including data acquisition and reconstruction. A measure of confidence in such estimated motion-fields is highly desirable, for instance to ensure the patient's safety in case of unexpected and undesirable motion. In this work, we propose a framework based on Gaussian Processes to infer 3D motion-fields and uncertainty maps in real-time from only three readouts of MR-data. We demonstrated an inference frame rate up to 69 Hz including data acquisition and reconstruction, thereby exploiting the limited amount of required MR-data. Additionally, we designed a rejection criterion based on the motion-field uncertainty maps to demonstrate the framework's potential for quality assurance. The framework was validated in silico and in vivo on healthy volunteer data (n=5) acquired using an MR-linac, thereby taking into account different breathing patterns and controlled bulk motion. Results indicate end-point-errors with a 75th percentile below 1mm in silico, and a correct detection of erroneous motion estimates with the rejection criterion. Altogether, the results show the potential of the framework for application in real-time MR-guided radiotherapy with an MR-linac., Comment: This manuscript has supplementary files which can be downloaded at https://surfdrive.surf. nl/files/index.php/s/scLts9nJYXfbLMx. The files include videos that show reconstructed motion-fields and spatial uncertainty maps. See the Appendix for a description of all individual files

The MR-Linac is a combination of an MR-scanner and radiotherapy linear accelerator (Linac) which holds the promise to increase the precision of radiotherapy treatments with MR-guided radiotherapy by monitoring motion during radiotherapy with MRI, and adjusting the radiotherapy plan accordingly. Optimal MR-guidance for respiratory motion during radiotherapy requires MR-based 3D motion estimation with a latency of 200-500 ms. Currently this is still challenging since typical methods rely on MR-images, and are therefore limited by the 3D MR-imaging latency. In this work, we present a method to perform non-rigid 3D respiratory motion estimation with 170 ms latency, including both acquisition and reconstruction. The proposed method called real-time low-rank MR-MOTUS reconstructs motion-fields directly from k-space data, and leverages an explicit low-rank decomposition of motion-fields to split the large scale 3D+t motion-field reconstruction problem posed in our previous work into two parts: (I) a medium-scale offline preparation phase and (II) a small-scale online inference phase which exploits the results of the offline phase for real-time computations. The method was validated on free-breathing data of five volunteers, acquired with a 1.5T Elekta Unity MR-Linac. Results show that the reconstructed 3D motion-field are anatomically plausible, highly correlated with a self-navigation motion surrogate (R = 0.975 +/- 0.0110), and can be reconstructed with a total latency of 170 ms that is sufficient for real-time MR-guided abdominal radiotherapy., Comment: This manuscript has supplementary files which can be downloaded at https://surfdrive.surf.nl/files/index.php/s/vz2xmwliglRmcjo. The files include six videos that show reconstructed motion-fields and a document with supporting figures. See Appendix I for a description of all individual files

Fast and accurate modeling of MR signal responses are typically required for various quantitative MRI applications, such as MR Fingerprinting and MR-STAT. This work uses a new EPG-Bloch model for accurate simulation of transient-state gradient-spoiled MR sequences, and proposes a Recurrent Neural Network (RNN) as a fast surrogate of the EPG-Bloch model for computing large-scale MR signals and derivatives. The computational efficiency of the RNN model is demonstrated by comparing with other existing models, showing one to three orders of acceleration comparing to the latest GPU-accelerated open-source EPG package. By using numerical and in-vivo brain data, two use cases, namely MRF dictionary generation and optimal experimental design, are also provided. Results show that the RNN surrogate model can be efficiently used for computing large-scale dictionaries of transient-states signals and derivatives within tens of seconds, resulting in several orders of magnitude acceleration with respect to state-of-the-art implementations. The practical application of transient-states quantitative techniques can therefore be substantially facilitated., Comment: Correct for typo errors

With the recent introduction of the MR-LINAC, an MR-scanner combined with a radiotherapy LINAC, MR-based motion estimation has become of increasing interest to (retrospectively) characterize tumor and organs-at-risk motion during radiotherapy. To this extent, we introduce low-rank MR-MOTUS, a framework to retrospectively reconstruct time-resolved non-rigid 3D+t motion-fields from a single low-resolution reference image and prospectively undersampled k-space data acquired during motion. Low-rank MR-MOTUS exploits spatio-temporal correlations in internal body motion with a low-rank motion model, and inverts a signal model that relates motion-fields directly to a reference image and k-space data. The low-rank model reduces the degrees-of-freedom, memory consumption and reconstruction times by assuming a factorization of space-time motion-fields in spatial and temporal components. Low-rank MR-MOTUS was employed to estimate motion in 2D/3D abdominothoracic scans and 3D head scans. Data were acquired using golden-ratio radial readouts. Reconstructed 2D and 3D respiratory motion-fields were respectively validated against time-resolved and respiratory-resolved image reconstructions, and the head motion against static image reconstructions from fully-sampled data acquired right before and right after the motion. Results show that 2D+t respiratory motion can be estimated retrospectively at 40.8 motion-fields-per-second, 3D+t respiratory motion at 7.6 motion-fields-per-second and 3D+t head-neck motion at 9.3 motion-fields-per-second. The validations show good consistency with image reconstructions. The proposed framework can estimate time-resolved non-rigid 3D motion-fields, which allows to characterize drifts and intra and inter-cycle patterns in breathing motion during radiotherapy, and could form the basis for real-time MR-guided radiotherapy., Comment: 18 pages main text, 8 main figures, 1 main table, 12 supporting videos, 2 supporting figures, 1 supporting information PDF. Submitted to Magnetic Resonance in Medicine as Full Paper

Magnetic resonance-electrical properties tomography (MR-EPT) is a technique used to estimate the conductivity and permittivity of tissues from MR measurements of the transmit magnetic field. Different reconstruction methods are available, however all these methods present several limitations which hamper the clinical applicability. Standard Helmholtz based MR-EPT methods are severely affected by noise. Iterative reconstruction methods such as contrast source inversion-EPT (CSI-EPT) are typically time consuming and are dependent on their initialization. Deep learning (DL) based methods require a large amount of training data before sufficient generalization can be achieved. Here, we investigate the benefits achievable using a hybrid approach, i.e. using MR-EPT or DL-EPT as initialization guesses for standard 3D CSI-EPT. Using realistic electromagnetic simulations at 3 T and 7 T, the accuracy and precision of hybrid CSI reconstructions are compared to standard 3D CSI-EPT reconstructions. Our results indicate that a hybrid method consisting of an initial DL-EPT reconstruction followed by a 3D CSI-EPT reconstruction would be beneficial. DL-EPT combined with standard 3D CSI-EPT exploits the power of data driven DL-based EPT reconstructions while the subsequent CSI-EPT facilitates a better generalization by providing data consistency., Comment: 8 pages, 4 figures, 1 table

Purpose: To investigate deep learning electrical properties tomography (EPT) for application on different simulated and in-vivo datasets including pathologies for obtaining quantitative brain conductivity maps. Methods: 3D patch-based convolutional neural networks were trained to predict conductivity maps from B1 transceive phase data. To compare the performance of DLEPT networks on different datasets, three datasets were used throughout this work, one from simulations and two from in-vivo measurements from healthy volunteers and cancer patients, respectively. At first, networks trained on simulations are tested on all datasets with different levels of homogeneous Gaussian noise introduced in training and testing. Secondly, to investigate potential robustness towards systematical differences between simulated and measured phase maps, in-vivo data with conductivity labels from conventional EPT is used for training. Results: High quality of reconstructions from networks trained on simulations with and without noise confirms the potential of deep learning for EPT. However, artifact encumbered results in this work uncover challenges in application of DLEPT to in-vivo data. Training DLEPT networks on conductivity labels from conventional EPT improves quality of results. This is argued to be caused by robustness to artifacts from image acquisition. Conclusions: Networks trained on simulations with added homogeneous Gaussian noise yield reconstruction artifacts when applied to in-vivo data. Training with realistic phase data and conductivity labels from conventional EPT allows for severely reducing these artifacts.

Magnetic resonance imaging (MRI) based electrical properties tomography (EPT) is the quantification of the conductivity and permittivity of different tissues. These electrical properties can be obtained through different reconstruction methods and can be used as a contrast mechanism. The work presented here continues from the two-dimensional CSI-EPT algorithm which was shown to work with two-dimensional Matlab based simulations. The existing CSI-EPT algorithm is reformulated to use the transceive phase rather than relying on the transceive phase assumption. This is achieved by implementing a forward problem, computing the receive phase, into the inverse minimization problem, i.e. retrieving the electrical properties. Furthermore, the radio frequency (RF) shield is numerically implemented to model the RF fields inside the MRI more accurately. Afterwards, the algorithm is tested with three-dimensional FDTD simulations to investigate if the two-dimensional CSI-EPT can retrieve the electrical properties for three-dimensional RF fields. Finally, an MR experiment with a phantom is performed to show the potential for this method. From the results of the two-dimensional Matlab simulations it is seen that CSI-EPT can reconstruct the electrical properties using MRI accessible quantities. In the three-dimensional simulations it is observed that the electrical properties are underestimated, nonetheless, CSI-EPT is more precise than the standard Helmholtz based methods. Finally, the first CSI-EPT results using measured data are shown. The results for the reconstruction using measured data were of the same quality as the results from the FDTD simulation.

MR-STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time domain data simultaneously, without relying on the FFT. To do this at high-resolution, specialized algorithms are required to solve the underlying large-scale non-linear optimisation problem. We propose a matrix-free and parallelized inexact Gauss-Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high performance computing cluster and is demonstrated to be able to generate high-resolution ($1mm \times 1mm$ in-plane resolution) quantitative parameter maps in simulation, phantom and in-vivo brain experiments. Reconstructed $T_1$ and $T_2$ values for the gel phantoms are in agreement with results from gold standard measurements and for the in-vivo experiments the quantitative values show good agreement with literature values. In all experiments short pulse sequences with robust Cartesian sampling are used for which conventional MR Fingerprinting reconstructions are shown to fail., Comment: Accepted by NMR in Biomedicine on 2019-12-05

Estimation of internal body motion with high spatio-temporal resolution can greatly benefit MR-guided radiotherapy/interventions and cardiac imaging, but remains a challenge to date. In image-based methods, where motion is indirectly estimated by reconstructing and co-registering images, a trade off between spatial and temporal resolution of the motion-fields has to be made due to the image reconstruction step. However, we observe that motion-fields are very compressible due to the spatial correlation of internal body motion. Therefore, reconstructing only motion-fields directly from surrogate signals or k-space data without the need for image reconstruction should require few data, and could eventually result in high spatio-temporal resolution motion-fields. In this work we introduce MR-MOTUS, a framework that makes exactly this possible. The two main innovations of this work are (1) a signal model that explicitly relates the k-space signal of a deforming object to general non-rigid motion-fields, and (2) model-based reconstruction of motion-fields directly from highly undersampled k-space data by solving the corresponding inverse problem. The signal model is derived by modeling a deforming object as a static reference object warped by dynamic motion-fields, such that the dynamic signal is given explicitly in terms of motion-fields. We validate the signal model through numerical experiments with an analytical phantom, and reconstruct motion-fields from retrospectively undersampled in-vivo data. Results show that the reconstruction quality is comparable to state-of-the-art image registration for undersampling factors as high as 63 for 3D non-rigid respiratory motion and as high as 474 for 3D rigid head motion., Comment: 5 supplementary figures (GIF) with results can be downloaded from https://surfdrive.surf.nl/files/index.php/s/GCEtiYBxyOnzxjv or from the arXiv page

Electrical properties (EPs) of tissues, conductivity and permittivity, are modulated by the ionic and water content, which change in presence of pathologies. Information on tissues EPs can be used e.g. as an endogenous biomarker in oncology. MR-Electrical Properties Tomography (MR-EPT) aims to reconstruct tissue EPs by solving an electromagnetic inverse problem relating MR measurements of the transmit radiofrequency RF field to the EPs. However, MR-EPT reconstructions highly suffer from noise in the RF field maps, which limits the clinical applicability. Instead of employing electromagnetic models posing strict requirements on the measured quantities, we propose a data driven approach where the inverse transformation is learned by means of a neural network. Supervised training of a conditional generative adversarial neural network was performed using simulated realistic RF field maps and realistic human head dielectric models. Deep learning EPT (DL-EPT) reconstructions are presented for in-silica MR data and MR measurements at 3 Tesla on phantoms and human brains. DL-EPT shows high quality EP maps, demonstrating good accuracy and greatly improved precision compared to conventional MR-EPT. Moreover, DL-EPT allows permittivity reconstructions at 3 Tesla, which is not possible with state-of-art MR-EPT techniques. The supervised learning-based approach leverages the strength of tailored electromagnetic simulations, allowing inclusion of a priori information (e.g. coil setup) and circumvention of inaccessible MR electromagnetic quantities. Since DL-EPT is highly noise-robust, the requirements for MRI data acquisitions can be relaxed, allowing faster acquisitions and higher resolutions. We believe that DL-EPT greatly improves the quality and applicability of EPT opening a new window for an endogenous biomarker in MRI diagnostics that reflects differences in ionic tissue content.

To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic-CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images to be used for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate, rectal and cervical cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was trained in a paired fashion on 2D transverse slices of 32 prostate cancer patients. The trained network was tested on the remaining patients to generate sCT images. For 30 patients in the test set, dose recalculations of the clinical plan were performed on sCT images. Dose distributions were evaluated comparing voxel-based dose differences, gamma and dose-volume histogram (DVH) analysis. The sCT generation required 5.6 s and 21 s for a single patient volume on a GPU and CPU, respectively. On average, sCT images resulted in a higher dose to the target of maximum 0.3%. Results suggest that accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis. The sCT generation was sufficiently fast to be integrated into an MR-guided radiotherapy workflow., Comment: Accepted for publication in Physics in Medicine and Biology, in press (2018)

Magnetic resonance fingerprinting (MRF) can successfully recover quantitative multi-parametric maps of human tissue in a very short acquisition time. Due to their pseudo-random nature, the large spatial undersampling artifacts can be filtered out by an exhaustive search over a pre-computed dictionary of signal fingerprints. This reconstruction approach is robust to large data-model discrepancies and is easy to implement. The curse of dimensionality and the intrinsic rigidity of such a precomputed dictionary approach can however limit its practical applicability. In this work, a method is presented to reconstruct balanced gradient-echo (GRE) acquisitions with established iterative algorithms for nonlinear least-squares, thus bypassing the dictionary computation and the exhaustive search. The global convergence of the iterative approach is investigated by studying the transient dynamic response of balanced GRE sequences and its effect on the minimization landscape. Experimental design criteria are derived which enforce sensitivity to the parameters of interest and successful convergence. The method is validated on simulated and experimentally acquired MRI data. Keywords: MR Fingerprinting, quantitative MRI, Bloch equation, nonlinear least squares, sequence design., Comment: This manuscript was submitted to IEEE Transactions on Medical Imaging on the 4th of July 2017

In this contribution we present numerical and experimental results of a parametric quantitative study of radiative dipole antennas in a phased array configuration for efficient body magnetic resonance imaging at 7T via parallel transmission. For magnetic resonance imaging (MRI) at ultrahigh fields (7T and higher) dipole antennas are commonly used in phased arrays, particularly for body imaging targets. This study reveals the effects of dipole positioning in the array (elevation of dipoles above the subject and inter-dipole spacing) on their mutual coupling, $B_1^{+}$ per $P_{acc}$ and $B_1^{+}$ per maximum local SAR efficiencies as well as the RF-shimming capability. The numerical and experimental results are obtained and compared for a homogeneous phantom as well as for a real human models confirmed by in-vivo experiments.

MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adversarial network (GAN) with unpaired MR and CT images. A GAN consisting of two synthesis convolutional neural networks (CNNs) and two discriminator CNNs was trained with cycle consistency to transform 2D brain MR image slices into 2D brain CT image slices and vice versa. Brain MR and CT images of 24 patients were analyzed. A quantitative evaluation showed that the model was able to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR and CT images., Comment: MICCAI 2017 Workshop on Simulation and Synthesis in Medical Imaging

Quantitative Magnetic Resonance Imaging (MRI) is based on a two-steps approach: estimation of the magnetic moments distribution inside the body, followed by a voxel-by-voxel quantification of the human tissue properties. This splitting simplifies the computations but poses several constraints on the measurement process, limiting its efficiency. Here, we perform quantitative MRI as a one step process; signal localization and parameter quantification are simultaneously obtained by the solution of a large scale nonlinear inversion problem based on first-principles. As a consequence, the constraints on the measurement process can be relaxed and acquisition schemes that are time efficient and widely available in clinical MRI scanners can be employed. We show that the nonlinear tomography approach is applicable to MRI and returns human tissue maps from very short experiments. Keywords: MR-STAT, quantitative MRI, nonlinear tomography, MR Fingerprinting, large scale inversion.

Metasurfaces are artificial electromagnetic boundaries or interfaces usually implemented as two-dimensional periodic structures with subwavelength periodicity and engineered properties of constituent unit cells. The electromagnetic bandgap (EBG) effect in metasurfaces prevents all surface modes from propagating in a certain frequency band. While metasurfaces provide a number of important applications in microwave antennas and antenna arrays, their features are also highly suitable for MRI applications. In this work we manufacture and experimentally study finite samples based on mushroom-type EBG metasurfaces and employ them for suppression of inter-element coupling in dipole transmit coil arrays for body imaging at 7T. We show experimentally that employment of the samples EBG leads to reduction of coupling between adjacent closely-spaced dipole antenna elements of a 7T transmit/receive body array, which reduces scattering losses in neighboring channels and thereby improves the B1+ efficiency. The setup consists of two fractionated dipole antennas previously designed by the authors for body imaging at 7 Tesla. These are placed on top of a body-mimicking phantom and equipped with the manufactured finite-size sample of the metasurface tuned to have EBG properties at the Larmor frequency of 298 MHz. To improve the detection range of the B1+ field distribution of the top elements, four additional elements were positioned along the bottom side of the phantom. Scattering matrix measurements show that coupling between the two top elements is indeed reduced while the measurements performed on a 7T MRI machine confirm the array's B1+ efficiency improvement due to reduced scattering losses. This study provides a tool for the decoupling of dipole antennas in ultrahigh field transmit arrays, possibly resulting in denser element placement and/or larger subject-element spacing.